Mobile personal sensing (MPS) is the assessment of real-time data in situ through a mobile phone. MPS is poised to revolutionize the ability to both assess and self-monitor HIV-transmission risk behaviors. The proposed five-year career award will provide W. Scott Comulada, Dr.P.H., a statistician working in the field of HIV prevention, with protected time and the methodological expertise in MPS to inform the design of MPS- based HIV interventions. During the proposed career award, he will develop innovative methods to integrate information from three data types related to HIV transmission risk over time: social network, diary, and Global Positioning Satellite (GPS) location trace data. Three years ago, Dr. Comulada received a doctoral degree in Biostatisics from the University of California, Los Angeles. He is currently the Associate Director of the Methods Core for the Center for HIV, Identification, Prevention, and Treatment Services (CHIPTS). Dr. Comulada's academic training and recent career path have focused on the development of statistical methodology that will further HIV research. MPS is moving HIV research away from traditional research methods that Dr. Comulada has expertise in. It is critical at this stage in his career that Dr. Comulada be afforded the time and training opportunities needed to become an independent investigator who is at the forefront of methodological development in HIV research. His training will focus on quantitative methods specific to social network and MPS data, the ethical conduct of research, and will build general skills for an academic career. The training plan includes structured coursework and mentoring by a group of experts in social networks, MPS, and HIV intervention design. The aims of the research are to: 1) Expand on established social network statistical models to handle assessment of real-time data in a MPS environment; 2) Develop models to predict behavioral outcomes using previous measurements of the outcome and previous / concurrently measured diary data and GPS location traces; and 3) Expand model from 2) to incorporate social network characteristics as a predictor of behavioral outcomes. A key component of the research will involve designing self-monitoring algorithms that will be based on his proposed statistical models and programmed into a mobile phone. For example, a mobile phone user could be alerted when they are in close proximity to a physical location that may lead to unwanted behavior, such as a bar. The mobile phone self-monitoring application will be pilot tested on persons living with HIV in Los Angeles County, California. Methodology developed from this study will be used by the applicant to inform the design of a MPS-based HIV intervention trial that will be developed in a subsequent R01 application.